Diagnostic Accuracy of Liquid Biomarkers in Airway Diseases: Toward Point-of-Care Applications
Bibliographic record
Abstract
Background: Liquid biomarkers have shown increasing utility in the clinical management of airway diseases. Salivary and blood samples are particularly amenable to point-of-care (POC) testing due to simple specimen collection and processing. However, very few POC tests have successfully progressed to clinical application due to the uncertainty and unpredictability surrounding their diagnostic accuracy. Objective: To review liquid biomarkers of airway diseases with well-established diagnostic accuracies and discuss their prospects for future POC applications. Methodology: A literature review of publications indexed in Medline or Embase was performed to evaluate the diagnostic accuracy of liquid biomarkers for chronic obstructive pulmonary disease (COPD), asthma, laryngopharyngeal reflux (LPR), and COVID-19. Results: Of 3,628 studies, 71 fulfilled the inclusion criteria. Sputum and blood eosinophils were the most frequently investigated biomarkers for the management of asthma and COPD. Salivary pepsin was the only biomarker with a well-documented accuracy for the diagnosis of LPR. Inflammatory blood biomarkers (e.g., CRP, D-dimers, ferritin) were found to be useful to predict the severity, complications, and mortality related to COVID-19 infection. Conclusion: Multiple liquid biomarkers have well-established diagnostic accuracies and are thus amenable to POC testing in clinical settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".